{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n",
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/sklearn/lda.py:6: DeprecationWarning: lda.LDA has been moved to discriminant_analysis.LinearDiscriminantAnalysis in 0.17 and will be removed in 0.19\n",
      "  \"in 0.17 and will be removed in 0.19\", DeprecationWarning)\n",
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/sklearn/learning_curve.py:23: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the functions are moved. This module will be removed in 0.20\n",
      "  DeprecationWarning)\n",
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/sklearn/qda.py:6: DeprecationWarning: qda.QDA has been moved to discriminant_analysis.QuadraticDiscriminantAnalysis in 0.17 and will be removed in 0.19.\n",
      "  \"in 0.17 and will be removed in 0.19.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import datetime \n",
    "from dateutil.parser import parse\n",
    "from datetime import date, timedelta\n",
    "from pandas.tseries.offsets import *\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import *\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import *\n",
    "from catboost import CatBoostRegressor\n",
    "\n",
    "from lightgbm import LGBMRegressor\n",
    "import pickle\n",
    "from lunar import *\n",
    "from copy import deepcopy\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data 集初步整理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_table('fusai_train_20180227.txt',encoding='gbk',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>brand</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8966</th>\n",
       "      <td>1107</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8967</th>\n",
       "      <td>1107</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8968</th>\n",
       "      <td>1107</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8969</th>\n",
       "      <td>1107</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8970</th>\n",
       "      <td>1107</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      date  day_of_week  brand  cnt\n",
       "8966  1107            4      3  235\n",
       "8967  1107            4      4  348\n",
       "8968  1107            4      5  350\n",
       "8969  1107            4      6  285\n",
       "8970  1107            4      7  232"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 日期分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_grouped = df_train.groupby(['date','day_of_week']).cnt.agg('sum').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date  day_of_week   cnt\n",
       "0     1            2    52\n",
       "1     2            3   104\n",
       "2     3            4    43\n",
       "3     4            5  7034\n",
       "4     5            6  5607"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_grouped['gap']=0\n",
    "\n",
    "for idx, row in df_grouped.iterrows():\n",
    "    if idx==0:\n",
    "        df_grouped.loc[idx,:]['gap']=1\n",
    "        continue\n",
    "    df_grouped.loc[idx, 'gap']=df_grouped.loc[idx,'day_of_week']-df_grouped.loc[idx-1,'day_of_week']\n",
    "    while df_grouped.loc[idx,'gap']<=0:\n",
    "         df_grouped.loc[idx,'gap']+=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f92e37c3dd8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IAACq0ZAGmB9jmpvnsInOp1hp7CEAAKDaPBrSMM4RvdfCmEOzNtt6sPJqBYfGmJuNMVcb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\n0ujLd9yYw1c9Qnrfz9fahkkLW6vHYWHljDupLht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857qSMcnL/JeU+GVznZWs3z1l3t1K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WSy0myew8y71MrUp23pJCvNgkKWyiKSlXZlDsUxvJ8qkUl1Wekqc7jcZWWBw5KsdMTBobB+kiUY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\n5zgWHy3clvU8TUeynU0Vl0fvb+H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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f933878e048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_grouped['gap'].plot(figsize=(15,8))\n",
    "df_grouped['cnt'].plot(figsize=(15,8), secondary_y=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "真实日期推测，技术圈里有相关文章我就不赘述了。数据集的第一天应该是2013年1月1日。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 补0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察数据可以发现有的date并非有所有的brand数据，这里把所有brand的数据都补上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "to_concat = {'date':[],\n",
    "            'day_of_week':[],\n",
    "            'brand':[],\n",
    "            'cnt':[]}\n",
    "for d in df_train['date'].unique():\n",
    "    df_temp = df_train[df_train['date']==d]\n",
    "    dow = df_temp.iloc[0,1]\n",
    "    for b in range(1,10):\n",
    "        if b not in df_temp['brand'].unique():\n",
    "            to_concat['date'].append(d)\n",
    "            to_concat['day_of_week'].append(dow)\n",
    "            to_concat['brand'].append(b)\n",
    "            to_concat['cnt'].append(0)\n",
    "df_train = df_train.append(pd.DataFrame(to_concat, columns=df_train.columns))\n",
    "df_train = df_train.sort_values(by='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10948, 4)\n"
     ]
    }
   ],
   "source": [
    "train_index = df_train.shape[0]\n",
    "train_y = df_train['cnt']\n",
    "print(df_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取a榜和b榜数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_test = pd.read_table('fusai_test_A_20180227.txt',encoding='gbk',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1107</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1107</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1107</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1107</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1108</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date  day_of_week  brand\n",
       "0  1107            4      7\n",
       "1  1107            4      8\n",
       "2  1107            4      9\n",
       "3  1107            4     10\n",
       "4  1108            5      1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_testa_answer = pd.read_table('fusai_answer_a_20180307.txt', sep='\\t',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1107</td>\n",
       "      <td>7</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1107</td>\n",
       "      <td>8</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1107</td>\n",
       "      <td>9</td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1107</td>\n",
       "      <td>10</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1108</td>\n",
       "      <td>1</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0   1    2\n",
       "0  1107   7  232\n",
       "1  1107   8  583\n",
       "2  1107   9  466\n",
       "3  1107  10  288\n",
       "4  1108   1  490"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_testa_answer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_testa_answer.rename(index=str, columns={0:'date',1:'brand',2:'cnt'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1289, 3)\n",
      "(1289, 3)\n"
     ]
    }
   ],
   "source": [
    "print(df_test.shape)\n",
    "print(df_testa_answer.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_test['cnt'] = df_testa_answer['cnt'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_index = df_test['date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_test2 = pd.read_table('fusai_test_B_20180227.txt',encoding='gbk',sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 合成全数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3541, 3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.concat([df_train, df_test, df_test2]) #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_index = df_train.shape[0]+df_test.shape[0]\n",
    "train_y = df['cnt'].iloc[0:train_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15778, 4)\n",
      "3541\n",
      "(12237,)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(df.shape[0]-train_index)\n",
    "print(train_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7034.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5607.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date  day_of_week     cnt\n",
       "0     1            2    52.0\n",
       "1     2            3   104.0\n",
       "2     3            4    43.0\n",
       "3     4            5  7034.0\n",
       "4     5            6  5607.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_grouped是对每天的所有品牌求和后的数据，方便后面提特征\n",
    "df_grouped = df[['date','day_of_week','cnt']].groupby(['date','day_of_week']).cnt.agg('sum').reset_index()\n",
    "df_grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_grouped = df_grouped.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# 首先是还原真实日期\n",
    "df_grouped['gap']=0\n",
    "\n",
    "for idx, row in df_grouped.iterrows():\n",
    "    if idx==0:\n",
    "        df_grouped.loc[idx,:]['gap']=1\n",
    "        continue\n",
    "    df_grouped.loc[idx, 'gap']=df_grouped.loc[idx,'day_of_week']-df_grouped.loc[idx-1,'day_of_week']\n",
    "    while df_grouped.loc[idx,'gap']<=0:\n",
    "         df_grouped.loc[idx,'gap']+=7\n",
    "\n",
    "df_grouped['virtual_date']=pd.NaT\n",
    "\n",
    "for idx, row in df_grouped.iterrows():\n",
    "    if idx==0:\n",
    "        d=datetime.date(2013,1,1)\n",
    "        while d.weekday()!=df_grouped.loc[idx,'day_of_week']-1:\n",
    "            d += DateOffset(days=1)\n",
    "        df_grouped.loc[idx, 'virtual_date']=d\n",
    "        continue\n",
    "    df_grouped.loc[idx, 'virtual_date']=df_grouped.loc[idx-1,'virtual_date']+DateOffset(days=row['gap'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>cnt</th>\n",
       "      <th>gap</th>\n",
       "      <th>virtual_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>1636</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-11-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636</th>\n",
       "      <td>1637</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-11-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>1638</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-11-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>1639</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-11-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>1640</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-11-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      date  day_of_week  cnt  gap virtual_date\n",
       "1635  1636            5  0.0    1   2017-11-24\n",
       "1636  1637            6  0.0    1   2017-11-25\n",
       "1637  1638            7  0.0    1   2017-11-26\n",
       "1638  1639            1  0.0    1   2017-11-27\n",
       "1639  1640            2  0.0    1   2017-11-28"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grouped.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get all the holidays\n",
    "holidays={'date':[],\n",
    "          'length':[]}\n",
    "for idx, row in df_grouped.iterrows():\n",
    "    if row['gap']>2:\n",
    "        holidays['length'].append(row['gap'])\n",
    "        holidays['date'].append(row['virtual_date']+DateOffset(days=-(row['gap']//2)))\n",
    "# convert to DataFrame\n",
    "holidays_df = pd.DataFrame(holidays, columns=['date','length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 年月日基础特征\n",
    "df_grouped['month']=df_grouped['virtual_date'].dt.month\n",
    "df_grouped['year']=df_grouped['virtual_date'].dt.year\n",
    "df_grouped['week']=df_grouped['virtual_date'].dt.week\n",
    "df_grouped['day']=df_grouped['virtual_date'].dt.day\n",
    "df_grouped['xun'] = df_grouped['day'].apply(lambda x: x//7)  # 第几周"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 农历年月日特征\n",
    "cvt = LunarSolarConverter()\n",
    "df_grouped['lunar_month']=df_grouped['virtual_date'].apply(lambda x: cvt.SolarToLunar(Solar(x.year,x.month,x.day)).lunarMonth)\n",
    "df_grouped['lunar_day']=df_grouped['virtual_date'].apply(lambda x: cvt.SolarToLunar(Solar(x.year,x.month,x.day)).lunarDay)\n",
    "df_grouped['lunar_year']=df_grouped['virtual_date'].apply(lambda x: cvt.SolarToLunar(Solar(x.year,x.month,x.day)).lunarYear)\n",
    "df_grouped['lunar_xun'] = df_grouped['lunar_day'].apply(lambda x: x//7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_grouped = df_grouped.drop('cnt', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.merge(df_grouped.drop('day_of_week',axis=1),on='date',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 只能统计train中数据\n",
    "df_temp = deepcopy(df.iloc[0:train_index,:])\n",
    "df_temp['cnt'] = train_y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  if __name__ == '__main__':\n",
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:15: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  from ipykernel import kernelapp as app\n",
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:20: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:27: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n"
     ]
    }
   ],
   "source": [
    "# 历史特征，这里先聚合出公历每月、农历每月、公历月精确到周，农历月精确到周的一些统计信息\n",
    "solar_cols = ['cnt','month','year','brand']\n",
    "solar_cols2 = ['cnt','month','year','brand','xun']\n",
    "lunar_cols = ['cnt','lunar_month','lunar_year','brand']\n",
    "lunar_cols2 = ['cnt','lunar_month','lunar_year','brand','lunar_xun']\n",
    "solar_history = pd.DataFrame(df_temp[solar_cols].groupby(['brand','month','year']).cnt.agg({'s_mean':'mean',\n",
    "#                                                                                     's_median':'median',\n",
    "                                                                                    's_skew':'skew',\n",
    "                                                                                    's_count':'count'})).reset_index()\n",
    "solar_history_xun = pd.DataFrame(df_temp[solar_cols2].groupby(['brand','month','year','xun']).cnt.agg({'sx_mean':'mean',\n",
    "                                                                                                'sx_median':'median',\n",
    "                                                                                                'sx_max':'max',\n",
    "                                                                                                'sx_min':'min',\n",
    "                                                                                                'sx_count':'count',\n",
    "                                                                                                'sx_std':'std'})).reset_index()\n",
    "\n",
    "lunar_history = pd.DataFrame(df_temp[lunar_cols].groupby(['brand','lunar_month', 'lunar_year']).cnt.agg({'l_mean':'mean',\n",
    "                                                                                                 'l_median':'median',\n",
    "                                                                                                 'l_count':'count',\n",
    "                                                                                                 'l_skew':'skew'})).reset_index()\n",
    "                             \n",
    "lunar_history_xun = pd.DataFrame(df_temp[lunar_cols2]).groupby(['brand','lunar_month','lunar_year','lunar_xun']).cnt.agg({'lx_mean':'mean',\n",
    "                                                                                                'lx_median':'median',\n",
    "                                                                                                'lx_max':'max',\n",
    "                                                                                                'lx_min':'min',\n",
    "                                                                                                'lx_count':'count',\n",
    "                                                                                                'lx_std':'std'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 修改列名，方便merge\n",
    "solar_history = solar_history.rename(index=str,columns= {'year':'last_year'})\n",
    "solar_history_xun = solar_history_xun.rename(index=str, columns={'year':'last_year'})\n",
    "lunar_history = lunar_history.rename(index=str, columns={'lunar_year':'last_lunar_year'})\n",
    "lunar_history_xun = lunar_history_xun.rename(index=str, columns={'lunar_year':'last_lunar_year'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 对日期做平移，默认平移一年，2016年10月11月的数据不存在，所以2017年相关数据平移两年\n",
    "df['last_year'] = df['year']-1\n",
    "df['last_lunar_year'] = df['lunar_year']-1\n",
    "df.loc[df['virtual_date']>date(2017,9,30),'last_year'] = df['year']-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15778, 17)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# merge\n",
    "df = df.merge(solar_history, on=['last_year','month','brand'],how='left')\n",
    "df = df.merge(solar_history_xun, on=['last_year','month','xun','brand'],how='left')\n",
    "df = df.merge(lunar_history, on=['last_lunar_year','lunar_month','brand'],how='left')\n",
    "df = df.merge(lunar_history_xun, on=['last_lunar_year','lunar_month','lunar_xun','brand'],how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 假期特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# holiday_v2.csv 是从网上爬的数据，只有一列特征，即该日是不是法定节假日\n",
    "holiday_df = pd.read_csv('holiday_v2.csv')\n",
    "holiday_df['virtual_date'] = pd.to_datetime(holiday_df['virtual_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.merge(holiday_df, on=['virtual_date'],how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 前后两天是不是法定节假日\n",
    "holiday_series = holiday_df.set_index('virtual_date')\n",
    "df['yesterday_hld'] = df['virtual_date'].apply(lambda x: holiday_series.loc[x+DateOffset(days=-1),'hld'])\n",
    "df['tomorrow_hld'] = df['virtual_date'].apply(lambda x: holiday_series.loc[x+DateOffset(days=1),'hld'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_month_type(x):\n",
    "    if x in [1,10]:\n",
    "        return 1\n",
    "    elif x in [9,12]:\n",
    "        return 2\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 这两个特征是我编的，把月份和品牌分成几类\n",
    "df['month_type'] = df['month'].apply(lambda x: get_month_type(x))\n",
    "\n",
    "df['brand_type'] = df['brand'].apply(lambda x: 1 if x in [5,8,9] else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 这是提取的另一个特征文件，包含以下几个特征\n",
    "# 该日是某段连续工作日中的正数第几天、倒数第几天\n",
    "# 该日距离春节、五一、国庆、元旦有几天\n",
    "data_feature = pd.read_csv('data_feature-v2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_feature['virtual_date'] = pd.to_datetime(data_feature['virtual_date'])\n",
    "df['virtual_date'] = pd.to_datetime(df['virtual_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.merge(data_feature, on='virtual_date',how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 最接近日"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15778, 51)\n",
      "(15778, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3737"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这是一个比较重要的特征，对于某一天day，我先计算出去年与它特性最接近的一天的日期，然后s_date_cnt是这个与它最接近日期的上牌量\n",
    "df_copy = df[['virtual_date','brand','cnt']].copy()\n",
    "\n",
    "df['s_date'] = pd.to_datetime(df['s_date'])\n",
    "\n",
    "df = df.merge(df_copy,left_on=['s_date','brand'],right_on=['virtual_date','brand'],how='left')\n",
    "\n",
    "print(df.shape)\n",
    "print(df_copy.shape)\n",
    "\n",
    "\n",
    "df = df.drop('virtual_date_y',axis=1).rename(index=str, columns={'virtual_date_x':'virtual_date',\n",
    "                                                                'cnt_y':'s_date_cnt',\n",
    "                                                                'cnt_x':'cnt'})\n",
    "\n",
    "df['s_date_cnt'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df2 = df[(df['virtual_date']<date(2015,10,12)) ] #& (df['virtual_date']>=date(2014,1,1))\n",
    "valid_df2 = df[(df['virtual_date']>=date(2015,10,12)) & (df['virtual_date']<date(2016,10,12))]\n",
    "\n",
    "train_X2 = train_df2.drop(['cnt','date','virtual_date','s_date'], axis=1)\n",
    "train_Y2 = train_df2['cnt']\n",
    "valid_X2 = valid_df2.drop(['cnt','date','virtual_date','s_date'], axis =1)\n",
    "valid_Y2 = valid_df2['cnt']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15778, 50)\n",
      "(3128,)\n",
      "(9109, 46)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(valid_Y2.shape)\n",
    "print(train_X2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['brand', 'day_of_week', 'gap', 'month', 'year', 'week', 'day', 'xun',\n",
       "       'lunar_month', 'lunar_day', 'lunar_year', 'lunar_xun', 'last_year',\n",
       "       'last_lunar_year', 's_mean', 's_skew', 's_count', 'sx_mean',\n",
       "       'sx_median', 'sx_max', 'sx_min', 'sx_count', 'sx_std', 'l_mean',\n",
       "       'l_median', 'l_count', 'l_skew', 'lx_mean', 'lx_median', 'lx_max',\n",
       "       'lx_min', 'lx_count', 'lx_std', 'hld', 'yesterday_hld', 'tomorrow_hld',\n",
       "       'month_type', 'brand_type', 'ith_workday', 'ith_workday_r',\n",
       "       'spring_distance', 'ld_distance', 'nd_distance', 'ny_distance',\n",
       "       'ny_distance2', 's_date_cnt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "month_idx = train_X2.columns.tolist().index('month')\n",
    "brandtype_idx = train_X2.columns.tolist().index('brand_type')\n",
    "monthtype_idx = train_X2.columns.tolist().index('month_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 尝试过加权重，提升不大\n",
    "weight = 1**(2016-train_X2['year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/lightgbm/basic.py:1029: UserWarning: categorical_feature in Dataset is overrided. New categorical_feature is ['brand_type', 'month', 'month_type']\n",
      "  warnings.warn('categorical_feature in Dataset is overrided. New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n",
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/lightgbm/basic.py:1029: UserWarning: categorical_feature in Dataset is overrided. New categorical_feature is ['brand_type', 'month', 'month_type']\n",
      "  warnings.warn('categorical_feature in Dataset is overrided. New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n"
     ]
    }
   ],
   "source": [
    "# 三种模型做ensamble，还做了bagging提升稳定性\n",
    "predict = 0\n",
    "bagging_num = 2\n",
    "for i in range(bagging_num): \n",
    "   model1 = ensemble.ExtraTreesRegressor(n_jobs=-1, n_estimators=1200, max_depth=10, max_features=0.6,\n",
    "                                          min_samples_split = 5, random_state = i*3)\n",
    "   model1.fit(train_X2.fillna(-999), train_Y2, sample_weight=weight.values)\n",
    "   predict +=  1*model1.predict(valid_X2.fillna(-999))\n",
    "    \n",
    "\n",
    "   model1 = LGBMRegressor(learning_rate=0.020, n_estimators=1200, subsample=0.8, max_depth=6, num_leaves=32,\n",
    "                          min_child_samples=5, colsample_bytree=0.5, reg_lambda=0.02, random_seed = i*3)\n",
    "   model1.fit(train_X2.fillna(-1), train_Y2, categorical_feature=['month_type','brand_type','month'],sample_weight=weight.values)\n",
    "   predict +=  model1.predict(valid_X2.fillna(-1))\n",
    "\n",
    "   model2 = CatBoostRegressor(iterations=1400, learning_rate=0.07, depth=4, l2_leaf_reg=3, loss_function='RMSE', \n",
    "                          eval_metric='RMSE',  logging_level='Silent', random_seed=i*5)\n",
    "   model2.fit(train_X2.fillna(-1), train_Y2,cat_features=[month_idx,brandtype_idx,monthtype_idx],eval_set=(valid_X2.fillna(-1),valid_Y2),\n",
    "             sample_weight = weight.values)\n",
    "   predict += model2.predict(valid_X2.fillna(-1))\n",
    "predict = predict/bagging_num/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133213151.676\n",
      "42587.324704703715\n"
     ]
    }
   ],
   "source": [
    "error = predict - valid_Y2\n",
    "metric= np.mean(np.square(error))\n",
    "print(np.sum(np.square(error)))\n",
    "print(metric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9311ddeef0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wiAuWAmwgrgB/bljs39EFTrceQQsAzpJgzFJ8dAgAMIgRLYA1ZhQEtg4jWgAAgwhaAACD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\nRNACABjk/wM4D66a+JFU5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9311ddef28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(error).hist(bins=80,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9311dde9e8>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R7YJ1D+ALVfVc4L+AdzXtd1TV/lX1DeCCqnpBVT0fWAX87477dwJeDrwWOKlp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9311aa21d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(model2.feature_importances_, index=train_X2.columns).plot.barh(figsize=(15,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df = df.iloc[:train_index,:]\n",
    "test_df = df.iloc[train_index:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12237, 50)\n",
      "(3541, 50)\n"
     ]
    }
   ],
   "source": [
    "print(train_df.shape)\n",
    "print(test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12237, 46)\n"
     ]
    }
   ],
   "source": [
    "train_X = train_df.drop(['cnt','date','virtual_date','s_date'], axis=1)\n",
    "train_Y = train_df['cnt']\n",
    "test_X = test_df.drop(['cnt','date','virtual_date','s_date'], axis =1)\n",
    "print(train_X.shape)\n",
    "month_idx = train_X.columns.tolist().index('month')\n",
    "brandtype_idx = train_X.columns.tolist().index('brand_type')\n",
    "monthtype_idx = train_X.columns.tolist().index('month_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/lightgbm/basic.py:1029: UserWarning: categorical_feature in Dataset is overrided. New categorical_feature is ['brand_type', 'month_type']\n",
      "  warnings.warn('categorical_feature in Dataset is overrided. New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 354.4657930\ttotal: 7.89ms\tremaining: 11s\n",
      "1:\tlearn: 339.4525800\ttotal: 15.6ms\tremaining: 10.9s\n",
      "2:\tlearn: 325.8727225\ttotal: 23.1ms\tremaining: 10.7s\n",
      "3:\tlearn: 313.2234266\ttotal: 30.3ms\tremaining: 10.6s\n",
      "4:\tlearn: 301.2077479\ttotal: 37.3ms\tremaining: 10.4s\n",
      "5:\tlearn: 290.5186718\ttotal: 44.3ms\tremaining: 10.3s\n",
      "6:\tlearn: 280.1630076\ttotal: 52.9ms\tremaining: 10.5s\n",
      "7:\tlearn: 270.5879442\ttotal: 62ms\tremaining: 10.8s\n",
      "8:\tlearn: 262.0331140\ttotal: 71.5ms\tremaining: 11s\n",
      "9:\tlearn: 253.9669186\ttotal: 80.1ms\tremaining: 11.1s\n",
      "10:\tlearn: 246.4382250\ttotal: 88.4ms\tremaining: 11.2s\n",
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     "text": [
      "/home/yuanhao/anaconda3/lib/python3.6/site-packages/lightgbm/basic.py:1029: UserWarning: categorical_feature in Dataset is overrided. New categorical_feature is ['brand_type', 'month_type']\n",
      "  warnings.warn('categorical_feature in Dataset is overrided. New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n"
     ]
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     ]
    }
   ],
   "source": [
    "predict = 0\n",
    "bagging_num = 2\n",
    "for i in range(bagging_num):\n",
    "    model1 = ensemble.ExtraTreesRegressor(n_jobs=-1, n_estimators=1200, max_depth=10, max_features=0.6,\n",
    "                                           min_samples_split = 5, random_state = i*3)\n",
    "    model1.fit(train_X.fillna(-999), train_Y)\n",
    "    predict +=  1*model1.predict(test_X.fillna(-999))\n",
    "    \n",
    "    model1 = LGBMRegressor(learning_rate=0.020, n_estimators=1200, subsample=0.8, max_depth=6, num_leaves=32, \n",
    "                           min_child_samples=5, colsample_bytree=0.35, reg_lambda=0.01, random_seed = i*3)\n",
    "    model1.fit(train_X.fillna(-1), train_Y, categorical_feature=['month_type','brand_type'])\n",
    "    predict +=  model1.predict(test_X.fillna(-1))\n",
    "\n",
    "    model2 = CatBoostRegressor(iterations=1400, learning_rate=0.06, depth=4, l2_leaf_reg=3, loss_function='RMSE', \n",
    "                           eval_metric='RMSE',  random_seed=i*5)\n",
    "    model2.fit(train_X.fillna(-1), train_Y,cat_features=[month_idx,monthtype_idx,brandtype_idx])\n",
    "    predict += model2.predict(test_X.fillna(-1))\n",
    "predict = predict/bagging_num/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ans_df = pd.DataFrame()\n",
    "ans_df['date'] = test_df['date']\n",
    "ans_df['brand'] = test_df['brand']\n",
    "ans_df['y'] = predict.astype(np.int)\n",
    "ans_df['y'].clip_lower(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ans_df_towrite = ans_df.drop_duplicates(subset=['date','brand'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ans_df_towrite.to_csv('fusaib-0308.txt',header=None,index=False, sep='\\t')"
   ]
  }
 ],
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